Design of a High-Performance Iterative Barrett Modular Multiplier for Crypto Systems

Modular multiplication (MM) is a fundamental operation in many cryptographic and arithmetic applications. In this article, we present an improved Barrett modular multiplication (BMM) algorithm and its hardware-efficient implementation. The proposed algorithm leverages parallel computation of quotien...

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Bibliographic Details
Published inIEEE transactions on very large scale integration (VLSI) systems Vol. 32; no. 5; pp. 897 - 910
Main Authors Zhang, Bo, Cheng, Zeming, Pedram, Massoud
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:Modular multiplication (MM) is a fundamental operation in many cryptographic and arithmetic applications. In this article, we present an improved Barrett modular multiplication (BMM) algorithm and its hardware-efficient implementation. The proposed algorithm leverages parallel computation of quotient and intermediate results, enhancing overall efficiency. To further optimize the algorithm, two optimizations are introduced, replacing expensive multiplications and additions with more efficient compression and encoding operations at each iteration. We first introduce a novel data model that enables the use of a 2-bit adder to handle potential overflow in signed addition. Moreover, by employing a 3-bit addition on intermediate results, we eliminate the need for complete round operations while ensuring the desired result range. The experimental results demonstrate significant improvements in terms of area and computation time compared to existing classic BMM and Montgomery modular multiplication (MMM) designs. Our improved BMM outperforms these designs, particularly in high-radix scenarios. This work provides a valuable contribution to the field of MM, offering a hardware-efficient solution for achieving improved performance in cryptographic and arithmetic systems.
ISSN:1063-8210
1557-9999
DOI:10.1109/TVLSI.2024.3368002